मिट्टी की गहराई 0-20 सेमी और 20-50 सेमी पर, निकालने योग्य पोटैशियम की मात्रा,
अनुमानित औसत और मानक विचलन.
पिक्सल वैल्यू को exp(x/10)-1 के साथ वापस बदलना होगा.
घने जंगल वाले इलाकों (आम तौर पर मध्य अफ़्रीका) में, मॉडल की सटीकता कम होती है. इसलिए, बैंडिंग (स्ट्राइपिंग) जैसे आर्टफ़ैक्ट दिख सकते हैं.
मिट्टी की प्रॉपर्टी के बारे में अनुमान, Innovative Solutions for Decision Agriculture Ltd. (iSDA) ने लगाए थे. इसके लिए, 30 मीटर पिक्सल साइज़ का इस्तेमाल किया गया था. साथ ही, रिमोट सेंसिंग डेटा के साथ मशीन लर्निंग का इस्तेमाल किया गया था. इसके अलावा, मिट्टी के 1,00,000 से ज़्यादा विश्लेषण किए गए सैंपल का ट्रेनिंग सेट भी इस्तेमाल किया गया था.
टॉम हेंगल, मिलर, एम॰ए॰ई॰, क्रिशनन, जे., वगैरह. अफ़्रीका की मिट्टी की प्रॉपर्टी और पोषक तत्वों को 30 मीटर के स्पेशल रिज़ॉल्यूशन पर मैप किया गया है. इसके लिए, दो-स्केल वाले एनसेंबल मशीन लर्निंग का इस्तेमाल किया गया है.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
मिट्टी की गहराई 0-20 सेमी और 20-50 सेमी पर, निकाला जा सकने वाला पोटाशियम, अनुमानित माध्य और मानक विचलन. पिक्सेल वैल्यू को exp(x/10)-1 फ़ॉर्मूले का इस्तेमाल करके वापस बदला जाना चाहिए. घने जंगल वाले इलाकों (आम तौर पर, मध्य अफ़्रीका) में, मॉडल की सटीकता कम होती है. इसलिए, बैंडिंग (स्ट्राइपिंग) जैसे आर्टफ़ैक्ट दिख सकते हैं. मिट्टी की प्रॉपर्टी के बारे में अनुमान …
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of extractable potassium in African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eThe data covers the period from 2001 to 2017 and was produced by Innovative Solutions for Decision Agriculture Ltd.(iSDA).\u003c/p\u003e\n"],["\u003cp\u003ePixel values require back-transformation using the formula \u003ccode\u003eexp(x/10)-1\u003c/code\u003e to obtain the actual potassium values in parts per million (ppm).\u003c/p\u003e\n"],["\u003cp\u003eThe dataset has a 30-meter resolution and may have lower accuracy with potential artifacts in dense jungle regions of central Africa.\u003c/p\u003e\n"],["\u003cp\u003eIt is licensed under CC-BY-4.0 and available for exploration and analysis within Google Earth Engine.\u003c/p\u003e\n"]]],["The dataset provides predicted mean and standard deviation of extractable potassium at 0-20 cm and 20-50 cm soil depths in Africa, from 2001 to 2017. iSDA generated the predictions using machine learning, remote sensing, and over 100,000 soil samples, at a 30m pixel size. Users must apply `exp(x/10)-1` to back-transform pixel values. Model accuracy is low in dense jungle areas, and the dataset is available on Google Earth Engine.\n"],null,["# iSDAsoil Extractable Potassium\n\nDataset Availability\n: 2001-01-01T00:00:00Z--2017-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [iSDA](https://isda-africa.com/)\n\nTags\n:\n [africa](/earth-engine/datasets/tags/africa) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \npotassium \n\n#### Description\n\nExtractable potassium at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Pixel Size | Description |\n|---------------|-------|-----|-----|------------|--------------------------------------------------------------|\n| `mean_0_20` | ppm | 1 | 80 | meters | Potassium, extractable, predicted mean at 0-20 cm depth |\n| `mean_20_50` | ppm | 0 | 79 | meters | Potassium, extractable, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | ppm | 0 | 92 | meters | Potassium, extractable, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | ppm | 0 | 92 | meters | Potassium, extractable, standard deviation at 20-50 cm depth |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\n### Citations\n\nCitations:\n\n- Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients\n mapped at 30 m spatial resolution using two-scale ensemble machine learning.\n Sci Rep 11, 6130 (2021).\n [doi:10.1038/s41598-021-85639-y](https://doi.org/10.1038/s41598-021-85639-y)\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar mean_0_20 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#0D0887\" label=\"0-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"32.1-43.7\" opacity=\"1\" quantity=\"38\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"43.7-48.4\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"48.4-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"53.6-59.3\" opacity=\"1\" quantity=\"41\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"59.3-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"65.7-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"72.7-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"89-98.5\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"98.5-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"108.9-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"120.5-133.3\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"133.3-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"163-199.3\" opacity=\"1\" quantity=\"53\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"163-1200\" opacity=\"1\" quantity=\"55\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar mean_20_50 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#0D0887\" label=\"0-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"32.1-43.7\" opacity=\"1\" quantity=\"38\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"43.7-48.4\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"48.4-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"53.6-59.3\" opacity=\"1\" quantity=\"41\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"59.3-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"65.7-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"72.7-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"89-98.5\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"98.5-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"108.9-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"120.5-133.3\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"133.3-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"163-199.3\" opacity=\"1\" quantity=\"53\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"163-1200\" opacity=\"1\" quantity=\"55\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar stdev_0_20 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#fde725\" label=\"low\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar stdev_20_50 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#fde725\" label=\"low\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/potassium_extractable\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Potassium extractable, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Potassium extractable, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Potassium extractable, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Potassium extractable, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 250};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Potassium extractable, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_potassium_extractable) \n[iSDAsoil Extractable Potassium](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_potassium_extractable) \nExtractable potassium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were ... \nISDASOIL/Africa/v1/potassium_extractable, africa,isda,soil \n2001-01-01T00:00:00Z/2017-01-01T00:00:00Z \n-35.22 -31.46 37.98 57.08 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://isda-africa.com/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_potassium_extractable)"]]
